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v6.4.3
8.5.0 (Latest)v8.3.0v8.1.0v7.2.4v6.4.3
  • Welcome to HEAVY.AI Documentation
  • Overview
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  • Installation and Configuration
    • System Requirements
      • Hardware Reference
      • Software Requirements
    • Installation
      • Free Version
      • Installing on Rocky Linux / RHEL
        • HEAVY.AI Installation on RHEL
        • Install NVIDIA Drivers and Vulkan on Rocky Linux and RHEL
      • Installing on Ubuntu
        • HEAVY.AI Installation on Ubuntu
        • Install NVIDIA Drivers and Vulkan on Ubuntu
      • Installing on Docker
        • HEAVY.AI Installation using Docker on Ubuntu
      • Getting Started on AWS
      • Getting Started on GCP
      • Getting Started on Azure
      • Upgrading
        • Upgrading HEAVY.AI
        • Upgrading from Omnisci to HEAVY.AI 6.0
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    • Configuration Parameters
      • Overview
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      • Connecting Using SAML
      • Implementing a Secure Binary Interface
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  • Loading and Exporting Data
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      • Kafka
      • Using Heavy Immerse Data Manager
      • Importing Geospatial Data
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  • SQL
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      • SQL Capabilities
        • ALTER SESSION SET
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        • DELETE
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        • SELECT
        • SHOW
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        • Arrays
        • Logical Operators and Conditional and Subquery Expressions
        • Table Expression and Join Support
        • Type Casts
      • Geospatial Capabilities
      • Functions and Operators
      • System Table Functions
        • generate_random_strings
        • generate_series
        • tf_compute_dwell_times
        • tf_feature_self_similarity
        • tf_feature_similarity
        • tf_geo_rasterize
        • tf_geo_rasterize_slope
        • tf_graph_shortest_path
        • tf_graph_shortest_paths_distances
        • tf_load_point_cloud
        • tf_mandelbrot*
        • tf_point_cloud_metadata
        • tf_raster_contour_lines; tf_raster_contour_polygons
        • tf_raster_graph_shortest_slope_weighted_path
        • tf_rf_prop_max_signal (Directional Antennas)
        • ts_rf_prop_max_signal (Isotropic Antennas)
        • tf_rf_prop
      • Window Functions
      • Reserved Words
      • SQL Extensions
  • Heavy Immerse
    • Introduction to Heavy Immerse
    • Admin Portal
    • Control Panel
    • Working with Dashboards
      • Dashboard List
      • Creating a Dashboard
      • Configuring a Dashboard
      • Duplicating and Sharing Dashboards
    • Measures and Dimensions
    • Using Parameters
    • Using Filters
    • Chart Animation
    • Multilayer Charts
    • SQL Editor
    • Customization
    • Chart Types
      • Overview
      • Bar
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      • Number
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      • Skew-T
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  • HeavyRF
    • Introduction to HeavyRF
    • Getting Started
    • HeavyRF Table Functions
  • HeavyConnect
    • HeavyConnect Release Overview
    • Getting Started
    • Best Practices
    • Examples
    • Command Reference
    • Parquet Data Wrapper Reference
    • ODBC Data Wrapper Reference
  • Python / Data Science
    • Data Science Foundation
    • JupyterLab Installation and Configuration
    • Using HEAVY.AI with JupyterLab
    • Python User-Defined Functions (UDFs) with the Remote Backend Compiler (RBC)
      • Installation
      • Registering and Using a Function
      • User-Defined Table Functions
      • RBC UDF/UDTF Example Notebooks
      • General UDF/UDTF Tutorial Notebooks
      • RBC API Reference
    • Ibis
    • Interactive Data Exploration with Altair
    • Additional Examples
      • Forecasting with HEAVY.AI and Prophet
  • APIs and Interfaces
    • Overview
    • heavysql
    • Thrift
    • JDBC
    • ODBC
    • Vega
      • Vega Tutorials
        • Vega at a Glance
        • Getting Started with Vega
        • Getting More from Your Data
        • Creating More Advanced Charts
        • Using Polys Marks Type
        • Vega Accumulator
        • Using Transform Aggregation
        • Improving Rendering with SQL Extensions
      • Vega Reference Overview
        • data Property
        • projections Property
        • scales Property
        • marks Property
      • Migration
        • Migrating Vega Code to Dynamic Poly Rendering
      • Try Vega
    • RJDBC
    • SQuirreL SQL
    • heavyai-connector
  • Tutorials and Demos
    • Loading Data
    • Using Heavy Immerse
    • Hello World
    • Creating a Kafka Streaming Application
    • Getting Started with Open Source
    • Try Vega
  • Troubleshooting and Special Topics
    • FAQs
    • Troubleshooting
    • Vulkan Renderer
    • Optimizing
    • Known Issues and Limitations
    • Logs and Monitoring
    • Archived Release Notes
      • Release 5.x
      • Release 4.x
      • Release 3.x
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  • Try It: Vega Editor
  • Simple Pointmap
  • Pointmap With Scales
  • Grouped Pointmap
  • Square Heatmap
  • Hexagon Heatmap
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  1. APIs and Interfaces
  2. Vega

Try Vega

PreviousMigrating Vega Code to Dynamic Poly RenderingNextRJDBC

Last updated 1 year ago

You can try the HEAVY.AI Vega engine and work with various examples. The database used for the examples includes over 100,000,000 rows. The data is based on the political donations dataset. Some columns have been stripped so that the data can fit on one NVIDIA Tesla P100 GPU.

Try It: Vega Editor

The following examples are available.

Simple Pointmap

Pointmap With Scales

Grouped Pointmap

Square Heatmap

Hexagon Heatmap